Business Support Engineer - AdTech

You will drive systemic improvements to support frameworks, influence product roadmaps through data-driven insights, and set the technical direction for a global team of Business Support Engineers specializing in Meta's AdTech ecosystem. This role requires deep expertise in API integrations, cloud infrastructure, and AI-native tooling to proactively identify and eliminate root causes of partner-impacting issues at scale.

Responsibilities

Architect and own global troubleshooting methodologies, escalation frameworks, and engineering standards that elevate the operational excellence of the Business Support Engineering organization Lead the team's AI-native technical strategy, driving large-scale adoption of AI and LLM-based tools to transform partner support workflows and accelerate issue resolution Design, build, and optimize complex AI architectures using large language models, owning the full lifecycle from prototype to production deployment for support automation use cases Define and implement performance monitoring systems for partner integrations across Meta's Ads and Business Messaging platforms, leveraging metrics to proactively surface and resolve systemic issues Partner with cross-functional leadership across Product, Infrastructure, and Partnership teams to identify root causes of recurring partner issues and influence product roadmaps with data-driven proposals Establish technical communication standards for the team, including authoring reference architectures, integration guides, and representing the engineering organization in partner-facing and internal executive forums Lead zero-to-one initiatives that anticipate future platform needs, designing scalable support infrastructure for new Meta business product launches and partner integration patterns Provide oncall support coverage via rotation schedule to ensure high availability and rapid response for partner-impacting incidents across global time zones Mentor and develop other engineers on the team, setting technical direction and fostering an environment of continuous learning in AI, ML, and AdTech domains Manage complex, cross-functional programs spanning multiple business areas, driving alignment across regional offices and partnership teams to deliver consistent partner experiences globally

Qualifications

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of experience as a software engineer or site reliability engineer Proven experience in API development on cloud-based infrastructures, being able to debug, identify root causes and resolve independently any outages that are impacting Meta Partners Experience with the full web stack, REST APIs, Python, PHP, and JavaScript/React development, along with debugging and bug management Experience working with large language models or AI-based tooling in a production or applied context Experience communicating with technical and business audiences and writing technical documentation Experience in assessing, analyzing, and resolving operational issues using data analysis (SQL) Experience in AdTech platforms, advertising APIs, or business messaging integrations, with familiarity in Meta's Ads or Business Products ecosystem Experience fine-tuning, optimizing, and deploying PyTorch-based models or LLM-powered agents at production scale Experience with open-source cloud infrastructure tooling such as Kubernetes, Docker, or Kubeflow in a partner integration or support engineering context Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Demonstrated track record of integrating AI tools to redesign support workflows and deliver measurable efficiency or quality improvements at an organizational level Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)